Climate Change and Public Health

University of Birmingham
n.dasandi@bham.ac.uk

University of Essex
s.mikhaylov@essex.ac.uk

The Lancet, University College London
n.watts@ucl.ac.uk

Abstract

We look at the link between public health issues and climate change discussed in the UN General Debates.

#Data

Loading data from the UNGDC data

#Loading packages and data
library(readtext)
library(quanteda)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(rworldmap)
library(RColorBrewer)
library(haven)
library(readxl)

DATA_DIR <- "~/Dropbox/Research/UNGDC projects/UN Data/" 

ungd_files <- readtext(paste0(DATA_DIR, "TXT/*"), 
                                 docvarsfrom = "filenames", 
                                 dvsep="_", 
                                 docvarnames = c("Country", "Session", "Year"))


ungd_files$doc_id <- str_replace(ungd_files$doc_id , ".txt", "") %>%
   str_replace(. , "_\\d{2}", "")

##Creating corpus object(s)

ungd_corpus <- corpus(ungd_files, text_field = "text") 

ungdc.2018 <- corpus_subset(ungd_corpus, Year==2018)

rr corp_summary <- summarise(group_by(summary(ungd_corpus, n = 8093),Year), total_speeches=n(),total_sentences=sum(Sentences),total_words=sum(Tokens)) readr::write_csv(corp_summary, _summary.csv)

#Pre-processing

Tokenizing corpus.

#Tokenization and basic pre-processing
tok <- tokens(ungd_corpus, what = "word",
              remove_punct = TRUE,
              remove_symbols = TRUE,
              remove_numbers = TRUE,
              remove_twitter = TRUE,
              remove_url = TRUE,
              remove_hyphens = FALSE,
              verbose = TRUE)

Starting tokenization...
...tokenizing 1 of 1 blocks
...preserving hyphens
...removing URLs
...indexing tokens: 88751 unique types
...total elapsed:  17.6 seconds.
Finished tokenizing and cleaning 8,640 texts.

Lowercasing and removing stopwords

tok <- tokens_tolower(tok)
tok.r <- tokens_select(tok, stopwords("english"), selection = "remove", padding = FALSE)

#Setting up agreed dictionaries

Creating compound tokens from the key terms (phrases) in our dictionaries:

mylist <- list( c("malaria"), c("diarrhoea"), c("infection"), c("disease"), c("sars"), c("measles"), c("pneumonia"), c("epidemic"), c("pandemic"), c("public", "health") , c("health","care"), c("epidemiology"), c("healthcare"), c("health"), c("mortality"), c("morbidity"), c("nutrition"), c("illness"), c("infectious"), c("ncd"), c("non-communicable", "disease"), c("noncommunicable","disease"), c("communicable", "disease"), c("air", "pollution"), c("nutrition"), c("malnutrition"), c("mental", "disorder"), c("stunting"), c("climate","change"), c("global","warming"), c("green","house"), c("temperature"), c("extreme","weather"), c("global", "environmental", "change"), c("climate","variability"), c("greenhouse"), c("low","carbon"), c("ghge"), c("renewable","energy"), c("carbon","emission"), c("co2","emission"), c("climate","pollutant")) 
 

tok.compound <- tokens_compound(tok.r, mylist, valuetype = "fixed", concatenator = "_")

Creating the dictionary of climate change terms:

climate_dict <- dictionary(list(climate =  c("climate_change", "global_warming", "green_house", "temperature", "extreme_weather", "global_environmental_change", "climate_variability", "greenhouse", "low_carbon", "ghge", "renewable_energy", "carbon_emission", "co2_emission", "climate_pollutant")))

Creating the dictionary of health terms:

health_dict <- dictionary(list(health = c("malaria", "diarrhoea", "infection", "disease", "sars", "measles", "pneumonia", "epidemic", "pandemic", "public_health", "health_care", "epidemiology", "healthcare", "health", "mortality", "morbidity", "nutrition", "illness", "infectious", "ncd", "non-communicable_disease", "noncommunicable_disease", "communicable_disease", "air_pollution", "nutrition", "malnutrition", "mental_disorder", "stunting")))

KWIC

Performing a keyword-in-context search. The window is set to 25 words before and after the term - reflecting approximately half a paragraph before and after the term (on average a paragraph in English is 50 words). UNGD speeches are highly structured documents drafted over long periods of time and reflect internal discussions and negotiations in government. Hence we assume that terms appearing within specific proximity of each other are related through the structured process of document generation.

We search for terms from our health dictionary and show the 25-word context around them.

tok.hea <- kwic(tok.compound, health_dict, window = 25, valuetype = "fixed")

readr::write_csv(tok.hea, "health_kwic_25_fixed.csv")

We search for terms from our climate change dictionary and show the 25-word context around them.

tok.cc <- kwic(tok.compound, climate_dict, window = 25, valuetype = "fixed")

readr::write_csv(tok.cc, "climate_kwic_25_fixed.csv")

To tap into the core proposition of this analysis - the link between climate change and health terms - we take the 25-word context around the health terms and search this context for climate change terms.

Text surrounding health terms (25 words each side) is saved and transformed to a corpus object. We then perform key-word-in-context search of this health related corpus for the terms in our climate change dictionary.

corpus_health <- corpus(tok.hea, split_context = FALSE, extract_keyword = TRUE)

tok.climate.kwic <- kwic(corpus_health, climate_dict, window = 25, valuetype = "fixed")

readr::write_csv(tok.climate.kwic, "intersection_kwic_25_fixed.csv")

Robustness analysis

For robustness analysis we looked at different context window sizes and manually assessed them for the balance of false positives vs false negatives.

rr tok.hea.5 <- kwic(tok.compound, health_dict, window = 5, valuetype = ) tok.cc.5 <- kwic(tok.compound, climate_dict, window = 5, valuetype = ) corpus_health.5 <- corpus(tok.hea.5, split_context = FALSE, extract_keyword = TRUE) tok.climate.kwic.5 <- kwic(corpus_health.5, climate_dict, window = 5, valuetype = ) readr::write_csv(tok.climate.kwic.5, _kwic_5_fixed.csv) readr::write_csv(tok.hea.5, _kwic_5_fixed.csv) readr::write_csv(tok.cc.5, _kwic_5_fixed.csv) tok.hea.10 <- kwic(tok.compound, health_dict, window = 10, valuetype = ) tok.cc.10 <- kwic(tok.compound, climate_dict, window = 10, valuetype = ) corpus_health.10 <- corpus(tok.hea.10, split_context = FALSE, extract_keyword = TRUE) tok.climate.kwic.10 <- kwic(corpus_health.10, climate_dict, window = 10, valuetype = ) readr::write_csv(tok.climate.kwic.10, _kwic_10_fixed.csv) readr::write_csv(tok.hea.10, _kwic_10_fixed.csv) readr::write_csv(tok.cc.10, _kwic_10_fixed.csv) tok.hea.30 <- kwic(tok.compound, health_dict, window = 30, valuetype = ) tok.cc.30 <- kwic(tok.compound, climate_dict, window = 30, valuetype = ) corpus_health.30 <- corpus(tok.hea.30, split_context = FALSE, extract_keyword = TRUE) tok.climate.kwic.30 <- kwic(corpus_health.30, climate_dict, window = 30, valuetype = ) readr::write_csv(tok.climate.kwic.30, _kwic_30_fixed.csv) readr::write_csv(tok.hea.30, _kwic_30_fixed.csv) readr::write_csv(tok.cc.30, _kwic_30_fixed.csv) tok.hea.50 <- kwic(tok.compound, health_dict, window = 50, valuetype = ) tok.cc.50 <- kwic(tok.compound, climate_dict, window = 50, valuetype = ) corpus_health.50 <- corpus(tok.hea.50, split_context = FALSE, extract_keyword = TRUE) tok.climate.kwic.50 <- kwic(corpus_health.50, climate_dict, window = 50, valuetype = ) readr::write_csv(tok.climate.kwic.50, _kwic_50_fixed.csv) readr::write_csv(tok.hea.50, _kwic_50_fixed.csv) readr::write_csv(tok.cc.50, _kwic_50_fixed.csv)

health_dfm <- dfm(corpus_health)

corpus_climate <- corpus(tok.cc, split_context = FALSE, extract_keyword = TRUE)
climate_dfm <- dfm(corpus_climate)

corpus_intersection <- corpus(tok.climate.kwic, split_context = FALSE, extract_keyword = TRUE)
intersection_dfm <- dfm(corpus_intersection)

health <- convert(health_dfm, "data.frame")
climate <- convert(climate_dfm, "data.frame")
intersection <- convert(intersection_dfm, "data.frame")

names(health)[1] <- "docid"
names(climate)[1] <- "docid"
names(intersection)[1] <- "docid"

health$"docid" <- str_extract(health$docid, "^([^.]+)")
climate$"docid" <- str_extract(climate$docid, "^([^.]+)")
intersection$"docid" <- str_extract(intersection$docid, "^([^.]+)")

health_counts <- health %>% group_by(docid) %>% summarise(health_count = n())
climate_counts <- climate %>% group_by(docid) %>% summarise(climate_count = n())
intersection_counts <- intersection %>% group_by(docid) %>% summarise(intersection_count = n())

total_counts <- climate_counts %>% 
  full_join(health_counts, by = "docid") %>% 
  full_join(intersection_counts, by = "docid") %>%
  replace_na(list(health_count=0, climate_count=0, intersection_count=0)) %>%
  separate(docid, c("country", "year"), "_")

total_counts$year <- as.numeric(total_counts$year)

total_counts <- arrange(total_counts, country, year)

readr::write_csv(total_counts, "total_counts.csv")

Maps of mention counts

Map for 2018 of PH and CC

Keeping only country-years with at least one mention of CC in PH conext

rr map <- joinCountryData2Map(subset(total_counts, year==2018 & health_count>0), joinCode=3, nameJoinColumn=)

100 codes from your data successfully matched countries in the map
0 codes from your data failed to match with a country code in the map
143 codes from the map weren't represented in your data

rr new_world <- subset(map, continent != ) pdf(_2018_health.pdf, width = 7, height = 3) par(mai=c(0,0,0.2,0),xaxs=,yaxs=) mapParams <- mapCountryData(new_world, nameColumnToPlot=_count, mapTitle=\2018 UN General Debate: Health, catMethod = , colourPalette = , oceanCol = , missingCountryCol = , addLegend=) do.call( addMapLegendBoxes, c(mapParams,title=of mentions,x = , horiz=TRUE, cex=0.5)) dev.off()

null device 
          1 

rr map <- joinCountryData2Map(subset(total_counts, year==2018 & climate_count>0), joinCode=3, nameJoinColumn=)

145 codes from your data successfully matched countries in the map
1 codes from your data failed to match with a country code in the map
98 codes from the map weren't represented in your data

rr new_world <- subset(map, continent != ) pdf(_2018_climate.pdf, width = 7, height = 3) par(mai=c(0,0,0.2,0),xaxs=,yaxs=) mapParams <- mapCountryData(new_world, nameColumnToPlot=_count, mapTitle=\2018 UN General Debate: Climate Change, catMethod = , colourPalette = , oceanCol = , missingCountryCol = , addLegend=) do.call( addMapLegendBoxes, c(mapParams,title=of mentions,x = , horiz=TRUE, cex=0.5)) dev.off()

null device 
          1 

rr map <- joinCountryData2Map(subset(total_counts, year==2018 & intersection_count>0), joinCode=3, nameJoinColumn=)

28 codes from your data successfully matched countries in the map
0 codes from your data failed to match with a country code in the map
215 codes from the map weren't represented in your data

rr new_world <- subset(map, continent != ) pdf(_2018_intersection.pdf, width = 7, height = 3) par(mai=c(0,0,0.2,0),xaxs=,yaxs=) mapParams <- mapCountryData(new_world, nameColumnToPlot=_count, mapTitle=\2018 UN General Debate: Intersection, catMethod = , colourPalette = , oceanCol = , missingCountryCol = , addLegend=) do.call( addMapLegendBoxes, c(mapParams,title=of mentions,x = , horiz=TRUE, cex=0.5)) dev.off()

null device 
          1 

Country plots

##USA

total_counts <- total_counts %>% add_row(country = "USA", year = 2018, climate_count = 0, health_count = 0, intersection_count  =  0)


ggplot(subset(total_counts, country=="USA"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("USA") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_USA.pdf")

Saving 7.29 x 4.51 in image

##GBR

total_counts <- total_counts %>% add_row(country = "GBR", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "GBR", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="GBR"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("UK") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_GBR.pdf")

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##Germany

total_counts <- total_counts %>% add_row(country = "DEU", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1973, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1974, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1975, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1976, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "DEU", year = 1977, climate_count = 0, health_count = 0, intersection_count  =  0)


ggplot(subset(total_counts, country=="DEU"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Germany") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_DEU.pdf")

Saving 7.29 x 4.51 in image

##France

ggplot(subset(total_counts, country=="FRA"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("France") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_FRA.pdf")

Saving 7.29 x 4.51 in image

##China

total_counts <- total_counts %>% add_row(country = "CHN", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) 

ggplot(subset(total_counts, country=="CHN"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("China") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_CHN.pdf")

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##Norway

total_counts <- total_counts %>% add_row(country = "NOR", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "NOR", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "NOR", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="NOR"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Norway") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_NOR.pdf")

Saving 7.29 x 4.51 in image

##Australia

total_counts <- total_counts %>% add_row(country = "AUS", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="AUS"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Australia") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_AUS.pdf")

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##India

total_counts <- total_counts %>% add_row(country = "IND", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "IND", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country=="IND"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("India") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_IND.pdf")

Saving 7.29 x 4.51 in image

##Belgium

total_counts <- total_counts %>% add_row(country = "BEL", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1973, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "BEL", year = 1974, climate_count = 0, health_count = 0, intersection_count  =  0) 

ggplot(subset(total_counts, country=="BEL"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Belgium") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_BEL.pdf")

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##Japan

total_counts <- total_counts %>% add_row(country = "JPN", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1972, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1973, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1974, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1975, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1976, climate_count = 0, health_count = 0, intersection_count  =  0) %>% add_row(country = "JPN", year = 1977, climate_count = 0, health_count = 0, intersection_count  =  0) 

ggplot(subset(total_counts, country=="JPN"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("Japan") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_JPN.pdf")

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#EU

#eu presidencies

total_counts$country_alt <- total_counts$country

total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 1970] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 1971] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 1972] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 1973] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 1974] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 1975] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 1976] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 1977] <- "EU"
total_counts$country_alt[total_counts$country== "DEU" & total_counts$year == 1978] <- "EU"
total_counts$country_alt[total_counts$country== "IRL" & total_counts$year == 1979] <- "EU"
total_counts$country_alt[total_counts$country== "LUX" & total_counts$year == 1980] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 1981] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 1982] <- "EU"
total_counts$country_alt[total_counts$country== "GRC" & total_counts$year == 1983] <- "EU"
total_counts$country_alt[total_counts$country== "IRL" & total_counts$year == 1984] <- "EU"
total_counts$country_alt[total_counts$country== "LUX" & total_counts$year == 1985] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 1986] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 1987] <- "EU"
total_counts$country_alt[total_counts$country== "GRC" & total_counts$year == 1988] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 1989] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 1990] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 1991] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 1992] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 1993] <- "EU"
total_counts$country_alt[total_counts$country== "DEU" & total_counts$year == 1994] <- "EU"
total_counts$country_alt[total_counts$country== "ESP" & total_counts$year == 1995] <- "EU"
total_counts$country_alt[total_counts$country== "IRL" & total_counts$year == 1996] <- "EU"
total_counts$country_alt[total_counts$country== "LUX" & total_counts$year == 1997] <- "EU"
total_counts$country_alt[total_counts$country== "AUT" & total_counts$year == 1998] <- "EU"
total_counts$country_alt[total_counts$country== "FIN" & total_counts$year == 1999] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 2000] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 2001] <- "EU"
total_counts$country_alt[total_counts$country== "DNK" & total_counts$year == 2002] <- "EU"
total_counts$country_alt[total_counts$country== "ITA" & total_counts$year == 2003] <- "EU"
total_counts$country_alt[total_counts$country== "NLD" & total_counts$year == 2004] <- "EU"
total_counts$country_alt[total_counts$country== "GBR" & total_counts$year == 2005] <- "EU"
total_counts$country_alt[total_counts$country== "FIN" & total_counts$year == 2006] <- "EU"
total_counts$country_alt[total_counts$country== "PRT" & total_counts$year == 2007] <- "EU"
total_counts$country_alt[total_counts$country== "FRA" & total_counts$year == 2008] <- "EU"
total_counts$country_alt[total_counts$country== "SWE" & total_counts$year == 2009] <- "EU"
total_counts$country_alt[total_counts$country== "BEL" & total_counts$year == 2010] <- "EU"

total_counts <- total_counts %>% add_row(country_alt = "EU", year = 1970, climate_count = 0, health_count = 0, intersection_count  =  0) %>%  add_row(country_alt = "EU", year = 1971, climate_count = 0, health_count = 0, intersection_count  =  0)

ggplot(subset(total_counts, country_alt=="EU"), aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= health_count), colour = "blue", alpha = 0.9, linetype="dotdash") +
  geom_line(aes(y= climate_count), colour = "darkgreen", alpha = 0.9, linetype="dashed") +
  geom_line(aes(y= intersection_count), colour = "red", alpha = 0.9) +
  ggtitle("EU") + 
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

ggsave("timeseries_all_total_count_EU.pdf")

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Time series of total counts plot

total counts

# calculating the total number of mentions by year
sum <- summarise(group_by(total_counts, year), 
                 sum_CC = sum(climate_count), sum_PH = sum(health_count), sum_int = sum(intersection_count),
                 mean_CC = mean(climate_count), mean_PH = mean(health_count), mean_int = mean(intersection_count))

There were 43 warnings (use warnings() to see them)

rr ggplot(sum, aes(x=year)) + theme_bw() + geom_line(aes(y= sum_PH), colour = , alpha = 0.9, linetype=) + geom_line(aes(y= sum_CC), colour = , alpha = 0.9, linetype=) + # geom_line(aes(y= count), colour = , alpha = 0.9) + #ggtitle(engagement with the intersection of climate change and health) + ylab(number of references per UNGD session) + xlab() + # scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))+ annotate(, x = 2000, y = 850, label = Change, colour = )+ annotate(, x = 1993, y = 250, label = , colour = ) ggsave(_PHCC_total_count.pdf)

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ggplot(sum, aes(x=year)) +
  theme_bw() +
  geom_line(aes(y= sum_int), colour = "red", alpha = 0.9) +
  ylab("Total number of references per UNGD session") + xlab("Year") + 
#  scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018)) +
   annotate("text", x = 2015, y = 20, label = "Intersection", colour = "red")

ggsave("timeseries_intersection_total_count.pdf")

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readr::write_csv(sum, "figure57.csv")

rr ggplot(sum, aes(x=year)) + theme_bw() + geom_line(aes(y= sum_PH), colour = , alpha = 0.9, linetype=) + geom_line(aes(y= sum_CC), colour = , alpha = 0.9, linetype=) + geom_line(aes(y= sum_int), colour = , alpha = 0.9) + #ggtitle(engagement with the intersection of climate change and health) + ylab(number of references per UNGD session) + xlab() + # scale_y_continuous(limits=c(0, 140), breaks = c(1, 50, 100, 134)) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))+ annotate(, x = 2000, y = 850, label = Change, colour = )+ annotate(, x = 1993, y = 250, label = , colour = ) + annotate(, x = 2015, y = 100, label = , colour = ) ggsave(_all_total_count.pdf)

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Proportion of countries presentation

Proportion of countries that had at least one mention

Data preparation

climate_speeches <- total_counts %>% filter(climate_count>0) %>% group_by(year) %>% tally(name = "climate_speeches")

There were 50 or more warnings (use warnings() to see the first 50)

health_speeches <- total_counts %>% filter(health_count>0) %>% group_by(year) %>% tally(name = "health_speeches")
intersection_speeches <- total_counts %>% filter(intersection_count>0) %>% group_by(year) %>% tally(name = "intersection_speeches")
total_speeches <- ungd_files %>% group_by(Year) %>% tally(name = "total_speeches")

proportions <- left_join(total_speeches, health_speeches, by = c("Year"="year")) %>% 
  left_join(., climate_speeches, by = c("Year"="year")) %>% 
  left_join(., intersection_speeches, by = c("Year"="year")) %>% 
  replace_na(list(health_speeches=0, climate_speeches=0, intersection_speeches=0))

proportions$percent_int <- proportions$intersection_speeches/proportions$total_speeches*100
proportions$percent_CC <- proportions$climate_speeches/proportions$total_speeches*100
proportions$percent_PH <- proportions$health_speeches/proportions$total_speeches*100

readr::write_csv(proportions, "figure28.csv")

Plotting the country proportion

rr ggplot(proportions, aes(x=Year, y=percent_CC, group = 1)) + geom_line(colour=, size=1) + theme_bw() + #ggtitle(engagement with the intersection of climate change and health) + ylab(of countries discussing climate change

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rr ggplot(proportions, aes(x=Year, y=percent_PH, group = 1)) + geom_line(colour=, size=1) + theme_bw() + #ggtitle(engagement with the intersection of climate change and health) + ylab(of countries discussing health

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rr ggplot(proportions, aes(x=Year, y=percent_int, group = 1)) + geom_line(colour=, size=1) + theme_bw() + #ggtitle(engagement with the intersection of climate change and health) + ylab(of countries discussing intersection

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ggplot(proportions, aes(x=Year)) +
  geom_line(aes(y=percent_int), colour="red", size=1) +
  geom_line(aes(y=percent_CC), colour="darkgreen", linetype = "dashed", size=1) +
  geom_line(aes(y=percent_PH), colour="blue", linetype = "dotdash", size=1) +
  theme_bw() +
  #ggtitle("Political engagement with the intersection of climate change and health") + 
  ylab("Proportion of countries, %") + xlab("Year") + 
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))+
 annotate("text", x = 2014, y = 30, label = "Intersection", colour = "red")+
  annotate("text", x = 1975, y = 55, label = "Health", colour = "blue") +
  annotate("text", x = 1988, y = 27, label = "Climate Change", colour = "darkgreen")

There were 50 or more warnings (use warnings() to see the first 50)

  ggsave("all_three_percent.pdf")

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#Tier Analysis

rr sids_countries <- c(,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,) tier1_countries <- c(,, ) tier2_countries <- c(,, , , , , , , ) total_counts\(country_groups <- ifelse(total_counts\)country %in% sids_countries, , ) total_counts\(country_groups[total_counts\)country %in% tier1_countries] <- 1
total_counts\(country_groups[total_counts\)country %in% tier2_countries] <- 2
#eu presidencies total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1970] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \ITA\ & total_counts\)year == 1971] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1972] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \DNK\ & total_counts\)year == 1973] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1974] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \ITA\ & total_counts\)year == 1975] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1976] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \BEL\ & total_counts\)year == 1977] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1978] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \IRL\ & total_counts\)year == 1979] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1980] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \GBR\ & total_counts\)year == 1981] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1982] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \GRC\ & total_counts\)year == 1983] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1984] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \LUX\ & total_counts\)year == 1985] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1986] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \DNK\ & total_counts\)year == 1987] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1988] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \FRA\ & total_counts\)year == 1989] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1990] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \NLD\ & total_counts\)year == 1991] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1992] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \BEL\ & total_counts\)year == 1993] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1994] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \ESP\ & total_counts\)year == 1995] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1996] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \LUX\ & total_counts\)year == 1997] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 1998] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \FIN\ & total_counts\)year == 1999] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 2000] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \BEL\ & total_counts\)year == 2001] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 2002] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \ITA\ & total_counts\)year == 2003] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 2004] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \GBR\ & total_counts\)year == 2005] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 2006] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \PRT\ & total_counts\)year == 2007] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 2008] <- \Tier1\ total_counts\)country_groups[total_counts\(country== \SWE\ & total_counts\)year == 2009] <- 1
total_counts\(country_groups[total_counts\)country==  & total_counts\(year == 2010] <- \Tier1\ ungd_files\)country_groups <- ifelse(ungd_files\(Country %in% sids_countries, \SIDS\, \none\) ungd_files\)country_groups[ungd_files$Country %in% tier1_countries] <- 1
ungd_files\(country_groups[ungd_files\)Country %in% tier2_countries] <- 2
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1970] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \ITA\ & ungd_files\)Year == 1971] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1972] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \DNK\ & ungd_files\)Year == 1973] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1974] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \ITA\ & ungd_files\)Year == 1975] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1976] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \BEL\ & ungd_files\)Year == 1977] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1978] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \IRL\ & ungd_files\)Year == 1979] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1980] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \GBR\ & ungd_files\)Year == 1981] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1982] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \GRC\ & ungd_files\)Year == 1983] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1984] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \LUX\ & ungd_files\)Year == 1985] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1986] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \DNK\ & ungd_files\)Year == 1987] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1988] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \FRA\ & ungd_files\)Year == 1989] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1990] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \NLD\ & ungd_files\)Year == 1991] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1992] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \BEL\ & ungd_files\)Year == 1993] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1994] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \ESP\ & ungd_files\)Year == 1995] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1996] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \LUX\ & ungd_files\)Year == 1997] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 1998] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \FIN\ & ungd_files\)Year == 1999] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 2000] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \BEL\ & ungd_files\)Year == 2001] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 2002] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \ITA\ & ungd_files\)Year == 2003] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 2004] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \GBR\ & ungd_files\)Year == 2005] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 2006] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \PRT\ & ungd_files\)Year == 2007] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files\(Year == 2008] <- \Tier1\ ungd_files\)country_groups[ungd_files\(Country== \SWE\ & ungd_files\)Year == 2009] <- 1
ungd_files\(country_groups[ungd_files\)Country==  & ungd_files$Year == 2010] <- 1

rr climate_speeches_groups <- total_counts %>% filter(climate_count>0) %>% group_by(year, country_groups) %>% tally(name = _speeches) health_speeches_groups <- total_counts %>% filter(health_count>0) %>% group_by(year, country_groups) %>% tally(name = _speeches) intersection_speeches_groups <- total_counts %>% filter(intersection_count>0) %>% group_by(year, country_groups) %>% tally(name = _speeches) total_speeches_groups <- ungd_files %>% group_by(Year, country_groups) %>% tally(name = _speeches) proportions_groups <- left_join(total_speeches_groups, health_speeches_groups, by = c(=, _groups)) %>% left_join(., climate_speeches_groups, by = c(=, _groups)) %>% left_join(., intersection_speeches_groups, by = c(=, _groups)) %>% replace_na(list(health_speeches=0, climate_speeches=0, intersection_speeches=0)) proportions_groups\(percent_int <- proportions_groups\)intersection_speeches/proportions_groups\(total_speeches*100 proportions_groups\)percent_CC <- proportions_groups\(climate_speeches/proportions_groups\)total_speeches100 proportions_groups\(percent_PH <- proportions_groups\)health_speeches/proportions_groups$total_speeches100 proportions_groups <- proportions_groups %>% filter(country_groups!=)

##Total count plots by country group

rr ggplot(proportions_groups, aes(x=Year, y=health_speeches, group=country_groups, linetype = country_groups, colour = country_groups)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle() + ylab(number of references per UNGD session) + xlab() + theme(legend.position=) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018)) ggsave(_total_by_country_group.pdf)

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rr ggplot(proportions_groups, aes(x=Year, y=climate_speeches, group=country_groups, linetype = country_groups, colour = country_groups)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(Change) + ylab(number of references per UNGD session) + xlab() + theme(legend.position=) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018)) ggsave(_total_by_country_group.pdf)

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rr ggplot(proportions_groups, aes(x=Year, y=intersection_speeches, group=country_groups, linetype = country_groups, colour = country_groups)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle() + ylab(number of references per UNGD session) + xlab() + theme(legend.position=) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018)) ggsave(_total_by_country_group.pdf)

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##Proportion plots by country group

rr ggplot(proportions_groups, aes(x=Year, y=percent_PH, group=country_groups, linetype = country_groups, colour = country_groups)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(of Health) + ylab(of Countries

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rr ggplot(proportions_groups, aes(x=Year, y=percent_CC, group=country_groups, linetype = country_groups, colour = country_groups)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(of Climate Change) + ylab(of Countries

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rr ggplot(proportions_groups, aes(x=Year, y=percent_int, group=country_groups, linetype = country_groups, colour = country_groups)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(of intersection) + ylab(of Countries

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#WHO regions

Total counts by WHO region

Using UN M49 Standard (Standard country or area codes for statistical use (M49)) we create regional and sub-regional groupings of countries. [https://unstats.un.org/unsd/methodology/m49/]

#loading WHO regions country identifiers and preprocessing
regions <- readr::read_csv("../UNSD - Methodology.csv")

Parsed with column specification:
cols(
  `Global Code` = col_double(),
  `Global Name` = col_character(),
  `Region Code` = col_double(),
  WHO = col_character(),
  `Region Name` = col_character(),
  `Sub-region Code` = col_double(),
  `Sub-region Name` = col_character(),
  `Intermediate Region Code` = col_double(),
  `Intermediate Region Name` = col_character(),
  `Country or Area` = col_character(),
  `M49 Code` = col_double(),
  `ISO-alpha3 Code` = col_character(),
  `Least Developed Countries (LDC)` = col_character(),
  `Land Locked Developing Countries (LLDC)` = col_character(),
  `Small Island Developing States (SIDS)` = col_character(),
  `Developed / Developing Countries` = col_character()
)
There were 50 or more warnings (use warnings() to see the first 50)

#UNGDC used YUG designation while WHO is using SRB
total_counts$country <- str_replace(total_counts$country, "YUG", "SRB")

#merging WHO designations with mention counts
regional_data <- inner_join(total_counts, regions, by = c("country" = "ISO-alpha3 Code"))

# total sum of mention counts by year and WHO region, with means
who_sum <- summarise(group_by(regional_data, year, WHO), 
                     who_int = sum(intersection_count), who_PH = sum(health_count), who_CC = sum(climate_count))

readr::write_csv(regional_data, "who_regions_data.csv")
readr::write_csv(who_sum, "figure58.csv")

Plot by region

rr ggplot(who_sum, aes(x=year, y=who_PH, group=WHO, linetype = WHO, colour = WHO)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle() + ylab(number of references per UNGD session) + xlab() + theme(legend.position=) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018)) ggsave(_total_by_who.pdf)

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rr ggplot(who_sum, aes(x=year, y=who_CC, group=WHO, linetype = WHO, colour = WHO)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(Change) + ylab(number of references per UNGD session) + xlab() + theme(legend.position=) + scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018)) ggsave(_total_by_who.pdf)

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ggplot(who_sum, aes(x=year, y=who_int, group=WHO, linetype = WHO, colour = WHO)) +
    geom_line() +
  guides(colour=guide_legend(title=NULL), linetype = FALSE) +
 guides(linetype=guide_legend(title=NULL)) +
  theme_bw() +
  ggtitle("Intersection") + 
  ylab("Total number of references per UNGD session") + xlab("Year") +
  theme(legend.position="bottom") +
  scale_x_continuous(limits=c(1970, 2018), breaks = c(1970, 1980, 1990, 2000, 2010, 2018))

  ggsave("intersection_total_by_who.pdf")

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Proportion by WHO region

Data preparation

climate_speeches_who <- regional_data %>% filter(climate_count>0) %>% group_by(year, WHO) %>% tally(name = "climate_speeches")
health_speeches_who <- regional_data %>% filter(health_count>0) %>% group_by(year, WHO) %>% tally(name = "health_speeches")
intersection_speeches_who <- regional_data %>% filter(intersection_count>0) %>% group_by(year, WHO) %>% tally(name = "intersection_speeches")

#merging WHO designations with mention counts
ungd_files$Country <- str_replace(ungd_files$Country, "YUG", "SRB")

total_speeches_who <- inner_join(ungd_files, regions, by = c("Country" = "ISO-alpha3 Code")) %>% 
  group_by(Year, WHO) %>% tally(name = "total_speeches")

proportions_who <- left_join(total_speeches_who, health_speeches_who, by = c("Year"="year", "WHO")) %>% 
  left_join(., climate_speeches_who, by = c("Year"="year", "WHO")) %>% 
  left_join(., intersection_speeches_who, by = c("Year"="year", "WHO")) %>% 
  replace_na(list(health_speeches=0, climate_speeches=0, intersection_speeches=0))

proportions_who$percent_int <- proportions_who$intersection_speeches/proportions_who$total_speeches*100
proportions_who$percent_CC <- proportions_who$climate_speeches/proportions_who$total_speeches*100
proportions_who$percent_PH <- proportions_who$health_speeches/proportions_who$total_speeches*100

readr::write_csv(proportions_who, "figure56.csv")

Plotting

rr ggplot(proportions_who, aes(x=Year, y=percent_PH, group=WHO, linetype = WHO, colour = WHO)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(of Health) + ylab(of Countries

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rr ggplot(proportions_who, aes(x=Year, y=percent_CC, group=WHO, linetype = WHO, colour = WHO)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(of Climate Change) + ylab(of Countries

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rr ggplot(proportions_who, aes(x=Year, y=percent_int, group=WHO, linetype = WHO, colour = WHO)) + geom_line() + guides(colour=guide_legend(title=NULL), linetype = FALSE) + guides(linetype=guide_legend(title=NULL)) + theme_bw() + ggtitle(of Intersection) + ylab(of countries

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